Modelling and trading the London, New York and Frankfurt stock exchanges with a new gene expression programming trader tool

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@Article{journals/isafm/Karathanasopoulos17,

• author = "Andreas Karathanasopoulos",
• title = "Modelling and trading the London, New York and Frankfurt stock exchanges with a new gene expression programming trader tool",
• journal = "Intelligent Systems in Accounting, Finance and Management",
• year = "2017",
• number = "1",
• volume = "24",
• pages = "3--11",
• keywords = "genetic algorithms, genetic programming, Gene Expression Programming",
• bibdate = "2017-05-28",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/isafm/isafm24.html#Karathanasopoulos17",
• DOI = "doi:10.1002/isaf.1401",
• abstract = "The scope of this manuscript is to present a new short-term financial forecasting and trading tool: the Gene Expression Programming (GEP) Trader Tool. It is based on the gene expression programming algorithm. This algorithm is based on a genetic programming approach, and provides supreme statistical and trading performance when used for modelling and trading financial time series. The GEP Trader Tool is offered through a user-friendly standalone Java interface. This paper applies the GEP Trader Tool to the task of forecasting and trading the future contracts of FTSE100, DAX30 and S&P500 daily closing prices from 2000 to 2015. It is the first time that gene expression programming has been used in such massive datasets. The model's performance is benchmarked against linear and nonlinear models such as random walk model, a moving-average convergence divergence model, an autoregressive moving average model, a genetic programming algorithm, a multilayer perceptron neural network, a recurrent neural network a higher order neural network. To gauge the accuracy of all models, both statistical and trading performances are measured. Experimental results indicate that the proposed approach outperforms all the others in the in-sample and out-of-sample periods by producing superior empirical results. Furthermore, the trading performances are improved further when trading strategies are imposed on each of the models.",

}

Genetic Programming entries for Andreas S Karathanasopoulos

Citations